Algorithms are instructions for the stepwise execution of a method. Social and cultural scientists but tend to broaden the meaning of this notion and use it as an umbrella notion for digital automatization in general. But computer programs contain non-algorithmic command syntax, also. Furthermore, algorithms may develop and change during implementation and use which makes talking about “the” algorithm being always identical with itself often difficult or impossible. A comprehension of the notion of algorithm too distant from that of computer science hinders the comprehensibility of social and cultural scientific analyses by computer scientists. On the other hand, these sciences shouldn’t confine their usage of this notion to that of the latter to be still able to deal with the phenomenon from a different perspective. (cf. Dourish 2016)
Automatic personality analysis doesn’t use data gathered by questionnaires administered to respondents, any more, but uses usage data which are generated by default and in different contexts, respectively. This is the big novelty of this field of investigation which led to the two articles published by Kosinski and Stillwell in the „Proceedings of the National Academy of Sciences of the United States of America“ in 2013 and 2015 being the most influential articles ever published in the “Proceedings” according to their Altmetric Score. These two articles dealt with the analysis of the personality of Facebook users using their Facebook likes. [...]
Table of Contents
1. Quantified Personality – Automatic Personality Analysis from Online and Mobile Usage Data
Objectives and Topics
This paper examines the emerging field of automatic personality analysis, which leverages Big Data from digital footprints—such as mobile usage logs, social media interactions, and textual content—to predict personality traits using the Big Five model. It explores the methodologies for extracting personality metrics, discusses the underlying Big Data paradoxes, and critically evaluates the practical applications and ethical risks associated with these predictive technologies.
- Evolution of automated personality analysis using Big Data
- Technical approaches for trait prediction via digital usage data
- Role of social media and mobile phone metadata in psychological profiling
- Ethical implications, including the risk of manipulation and the emergence of "personality bubbles"
- Sociological perspectives on the contingency of knowledge in the modern digital age
Excerpt from the Book
Quantified Personality – Automatic Personality Analysis from Online and Mobile Usage Data
Algorithms are instructions for the stepwise execution of a method. Social and cultural scientists but tend to broaden the meaning of this notion and use it as an umbrella notion for digital automatization in general. But computer programs contain non-algorithmic command syntax, also. Furthermore, algorithms may develop and change during implementation and use which makes talking about “the” algorithm being always identical with itself often difficult or impossible. A comprehension of the notion of algorithm too distant from that of computer science hinders the comprehensibility of social and cultural scientific analyses by computer scientists. On the other hand, these sciences shouldn’t confine their usage of this notion to that of the latter to be still able to deal with the phenomenon from a different perspective. (cf. Dourish 2016)
Automatic personality analysis doesn’t use data gathered by questionnaires administered to respondents, any more, but uses usage data which are generated by default and in different contexts, respectively. This is the big novelty of this field of investigation which led to the two articles published by Kosinski and Stillwell in the „Proceedings of the National Academy of Sciences of the United States of America“ in 2013 and 2015 being the most influential articles ever published in the “Proceedings” according to their Altmetric Score. These two articles dealt with the analysis of the personality of Facebook users using their Facebook likes.
Since automated personality analysis is based on Big Data - for the development of its methods as well as their application - it is subject to the three paradoxes of Big Data described by Richards and King (2013): 1. The transparency paradox which follows from collecting more and more data to be able to make the world more transparent, but collecting them in more and more invisible and opaque ways. The data are saved on servers in unknown places and are analysed by methods and algorithms which are not easy to scrutinize. This holds in general, i.e. also for data and methods of automatic personality analysis.
Summary of Chapters
1. Quantified Personality – Automatic Personality Analysis from Online and Mobile Usage Data: This opening section defines the foundational concepts of algorithmic analysis and introduces the shift from traditional questionnaire-based personality assessment to automated methods utilizing Big Data, while highlighting the inherent paradoxes of transparency, identity, and power.
Keywords
Automatic Personality Analysis, Big Five Model, Big Data, Digital Footprints, Mobile Usage Data, Facebook Likes, Psychography, Micro-targeting, Filter Bubble, Personality Bubble, Algorithms, Machine Learning, Data-driven Research, Ethical Risks, Social Media Analysis
Frequently Asked Questions
What is the core focus of this publication?
The work focuses on the technical and sociological aspects of using Big Data—generated through digital interactions—to automatically infer individual personality traits based on the Big Five psychological model.
Which central themes are discussed in the paper?
Key themes include the methodologies for predicting personality from smartphone and social media data, the practical use cases for commercial and political purposes, and the associated ethical and societal risks.
What is the primary research goal?
The primary goal is to analyze how automated personality assessment methods are constructed, their accuracy compared to traditional human judgment, and the implications of integrating these predictions into digital user environments.
What scientific methodology is utilized by the researchers?
The studies reviewed primarily employ regression analysis and machine learning algorithms (such as decision trees) to map digital usage patterns, like word frequencies or social network activity, to established Big Five personality traits.
What does the main body of the paper cover?
The main body examines different categories of input data—mobile usage, social media profiles, and text data—and provides a critical review of landmark studies, such as those by Kosinski and Stillwell, while addressing the limitations of these models.
Which keywords best characterize the work?
Key terms include Automatic Personality Analysis, Big Five Model, Big Data, Digital Footprints, Psychography, and Micro-targeting.
What is meant by the "identity paradox" mentioned in the text?
The identity paradox refers to the phenomenon where vast amounts of behavioral data are analyzed to reveal a user’s personality, only for the findings to be used to influence and modify those same behaviors, potentially trapping users in a "personality bubble."
How does the author evaluate the future role of virtual agents?
The author suggests that virtual agents and robots could use personality analysis to better adapt their social behavior to human partners, potentially creating a new ontological category of machines that possess human-like traits and high analytical sensibility.
Why does the text mention Cambridge Analytica?
Cambridge Analytica is discussed as a prominent example of the real-world application—and subsequent controversy—of psychographic voter analysis, highlighting the public interest and the potential for the abuse of personality data in political campaigns.
Is the Big Five personality model considered effective for current automated analysis?
Yes, the Big Five model remains the standard for automated analysis due to its high degree of external validation across various life domains, though the author notes that researchers must monitor for potential shifts in the predictive power of variables over time.
- Quote paper
- Steffen Schumacher (Author), 2017, Quantified Personality. Automatic Personality Analysis from Online and Mobile Usage Data, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/383163
